The 3D semantic labeling task involves predicting a semantic labeling of a 3D scan mesh.

Evaluation and metrics

Our evaluation ranks all methods according to the PASCAL VOC intersection-over-union metric (IoU). IoU = TP/(TP+FP+FN), where TP, FP, and FN are the numbers of true positive, false positive, and false negative pixels, respectively. Predicted labels are evaluated per-vertex over the respective 3D scan mesh; for 3D approaches that operate on other representations like grids or points, the predicted labels should be mapped onto the mesh vertices (e.g., one such example for grid to mesh vertices is provided in the evaluation helpers).



This table lists the benchmark results for the 3D semantic label scenario.


Method Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
Mix3Dpermissive0.781 10.964 10.855 10.843 100.781 10.858 70.575 20.831 150.685 50.714 10.979 10.594 30.310 150.801 10.892 70.841 20.819 30.723 20.940 70.887 10.725 9
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, Francis Engelmann: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. 3DV 2021 (Oral)
OccuSeg+Semantic0.764 20.758 400.796 150.839 110.746 70.907 10.562 30.850 100.680 60.672 50.978 20.610 10.335 70.777 30.819 260.847 10.830 10.691 60.972 10.885 20.727 7
O-CNNpermissive0.762 30.924 20.823 40.844 90.770 20.852 90.577 10.847 110.711 10.640 130.958 80.592 40.217 500.762 90.888 80.758 70.813 50.726 10.932 130.868 80.744 3
Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, Xin Tong: O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis. SIGGRAPH 2017
PointFormerV20.752 40.742 470.809 100.872 10.758 30.860 60.552 40.891 50.610 220.687 20.960 70.559 120.304 180.766 70.926 20.767 50.797 130.644 150.942 50.876 70.722 11
PointConvFormer0.749 50.793 260.790 180.807 200.750 60.856 80.524 100.881 60.588 320.642 120.977 40.591 50.274 290.781 20.929 10.804 30.796 140.642 160.947 30.885 20.715 13
Wenxuan Wu, Qi Shan, Li Fuxin: PointConvFormer: Revenge of the Point-based Convolution.
BPNetcopyleft0.749 50.909 30.818 70.811 170.752 50.839 130.485 240.842 130.673 70.644 100.957 100.528 190.305 170.773 50.859 130.788 40.818 40.693 50.916 150.856 140.723 10
Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong: Bidirectional Projection Network for Cross Dimension Scene Understanding. CVPR 2021 (Oral)
MSP0.748 70.623 710.804 110.859 30.745 80.824 240.501 160.912 20.690 40.685 30.956 110.567 90.320 120.768 60.918 30.720 160.802 90.676 80.921 140.881 40.779 1
StratifiedFormerpermissive0.747 80.901 40.803 120.845 80.757 40.846 110.512 130.825 160.696 30.645 90.956 110.576 70.262 370.744 140.861 120.742 90.770 270.705 30.899 250.860 110.734 4
Xin Lai*, Jianhui Liu*, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia: Stratified Transformer for 3D Point Cloud Segmentation. CVPR 2022
Virtual MVFusion0.746 90.771 340.819 60.848 60.702 190.865 50.397 620.899 30.699 20.664 60.948 330.588 60.330 80.746 130.851 180.764 60.796 140.704 40.935 100.866 90.728 5
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi, David Ross, Brian Brewington, Thomas Funkhouser, Caroline Pantofaru: Virtual Multi-view Fusion for 3D Semantic Segmentation. ECCV 2020
VMNetpermissive0.746 90.870 90.838 20.858 40.729 120.850 100.501 160.874 70.587 330.658 70.956 110.564 100.299 190.765 80.900 50.716 190.812 60.631 210.939 80.858 120.709 14
Zeyu HU, Xuyang Bai, Jiaxiang Shang, Runze Zhang, Jiayu Dong, Xin Wang, Guangyuan Sun, Hongbo Fu, Chiew-Lan Tai: VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation. ICCV 2021 (Oral)
Retro-FPN0.744 110.842 160.800 130.767 340.740 90.836 160.541 60.914 10.672 80.626 140.958 80.552 130.272 300.777 30.886 90.696 260.801 100.674 90.941 60.858 120.717 12
EQ-Net0.743 120.620 720.799 140.849 50.730 110.822 260.493 220.897 40.664 90.681 40.955 150.562 110.378 10.760 100.903 40.738 100.801 100.673 100.907 180.877 50.745 2
Zetong Yang*, Li Jiang*, Yanan Sun, Bernt Schiele, Jiaya JIa: A Unified Query-based Paradigm for Point Cloud Understanding. CVPR 2022
MinkowskiNetpermissive0.736 130.859 120.818 70.832 120.709 160.840 120.521 120.853 90.660 110.643 110.951 230.544 140.286 250.731 150.893 60.675 320.772 250.683 70.874 450.852 160.727 7
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019
IPCA0.731 140.890 50.837 30.864 20.726 130.873 20.530 90.824 170.489 650.647 80.978 20.609 20.336 60.624 300.733 390.758 70.776 230.570 450.949 20.877 50.728 5
SparseConvNet0.725 150.647 670.821 50.846 70.721 140.869 30.533 80.754 350.603 280.614 170.955 150.572 80.325 100.710 160.870 100.724 140.823 20.628 220.934 110.865 100.683 19
MatchingNet0.724 160.812 230.812 90.810 180.735 100.834 170.495 210.860 80.572 390.602 240.954 170.512 220.280 260.757 110.845 210.725 130.780 210.606 300.937 90.851 170.700 16
INS-Conv-semantic0.717 170.751 430.759 300.812 160.704 180.868 40.537 70.842 130.609 240.608 200.953 190.534 150.293 210.616 310.864 110.719 180.793 170.640 170.933 120.845 210.663 23
contrastBoundarypermissive0.705 180.769 370.775 240.809 190.687 210.820 290.439 490.812 220.661 100.591 270.945 420.515 210.171 680.633 270.856 140.720 160.796 140.668 110.889 330.847 190.689 18
Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao: Contrastive Boundary Learning for Point Cloud Segmentation. CVPR2022
RFCR0.702 190.889 60.745 390.813 150.672 240.818 330.493 220.815 200.623 180.610 180.947 360.470 340.249 420.594 350.848 190.705 230.779 220.646 140.892 310.823 280.611 39
Jingyu Gong, Jiachen Xu, Xin Tan, Haichuan Song, Yanyun Qu, Yuan Xie, Lizhuang Ma: Omni-Supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning. CVPR2021
One Thing One Click0.701 200.825 200.796 150.723 410.716 150.832 180.433 510.816 180.634 160.609 190.969 60.418 600.344 40.559 470.833 230.715 200.808 70.560 490.902 220.847 190.680 20
JSENetpermissive0.699 210.881 80.762 280.821 130.667 250.800 460.522 110.792 270.613 200.607 210.935 620.492 270.205 550.576 410.853 160.691 270.758 320.652 130.872 480.828 250.649 28
Zeyu HU, Mingmin Zhen, Xuyang BAI, Hongbo Fu, Chiew-lan Tai: JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds. ECCV 2020
PicassoNet-IIpermissive0.696 220.704 570.790 180.787 260.709 160.837 140.459 340.815 200.543 490.615 160.956 110.529 170.250 400.551 520.790 310.703 240.799 120.619 250.908 170.848 180.700 16
Huan Lei, Naveed Akhtar, Mubarak Shah, and Ajmal Mian: Geometric feature learning for 3D meshes.
One-Thing-One-Click0.693 230.743 460.794 170.655 650.684 220.822 260.497 200.719 450.622 190.617 150.977 40.447 470.339 50.750 120.664 540.703 240.790 190.596 340.946 40.855 150.647 29
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu: One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation. CVPR 2021
CU-Hybrid Net0.693 230.596 760.789 200.803 220.677 230.800 460.469 280.846 120.554 470.591 270.948 330.500 250.316 130.609 320.847 200.732 110.808 70.593 370.894 290.839 220.652 27
Feature_GeometricNetpermissive0.690 250.884 70.754 340.795 250.647 300.818 330.422 530.802 250.612 210.604 220.945 420.462 380.189 630.563 460.853 160.726 120.765 280.632 200.904 200.821 310.606 43
Kangcheng Liu, Ben M. Chen: https://arxiv.org/abs/2012.09439. arXiv Preprint
FusionNet0.688 260.704 570.741 430.754 380.656 260.829 200.501 160.741 400.609 240.548 350.950 270.522 200.371 20.633 270.756 340.715 200.771 260.623 230.861 550.814 330.658 24
Feihu Zhang, Jin Fang, Benjamin Wah, Philip Torr: Deep FusionNet for Point Cloud Semantic Segmentation. ECCV 2020
Feature-Geometry Netpermissive0.685 270.866 100.748 360.819 140.645 320.794 510.450 390.802 250.587 330.604 220.945 420.464 370.201 580.554 490.840 220.723 150.732 410.602 320.907 180.822 300.603 46
KP-FCNN0.684 280.847 150.758 320.784 280.647 300.814 360.473 260.772 300.605 260.594 260.935 620.450 450.181 660.587 360.805 290.690 280.785 200.614 260.882 370.819 320.632 34
H. Thomas, C. Qi, J. Deschaud, B. Marcotegui, F. Goulette, L. Guibas.: KPConv: Flexible and Deformable Convolution for Point Clouds. ICCV 2019
VACNN++0.684 280.728 530.757 330.776 300.690 200.804 440.464 320.816 180.577 380.587 290.945 420.508 240.276 280.671 170.710 440.663 370.750 350.589 400.881 390.832 240.653 26
Superpoint Network0.683 300.851 140.728 480.800 240.653 280.806 420.468 290.804 230.572 390.602 240.946 390.453 440.239 450.519 580.822 240.689 300.762 300.595 360.895 280.827 260.630 35
PointContrast_LA_SEM0.683 300.757 410.784 210.786 270.639 340.824 240.408 570.775 290.604 270.541 370.934 660.532 160.269 330.552 500.777 320.645 470.793 170.640 170.913 160.824 270.671 21
VI-PointConv0.676 320.770 360.754 340.783 290.621 380.814 360.552 40.758 330.571 410.557 330.954 170.529 170.268 350.530 560.682 490.675 320.719 440.603 310.888 340.833 230.665 22
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern, Li Fuxin: The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions.
ROSMRF3D0.673 330.789 270.748 360.763 360.635 360.814 360.407 590.747 370.581 370.573 300.950 270.484 280.271 320.607 330.754 350.649 420.774 240.596 340.883 360.823 280.606 43
SALANet0.670 340.816 220.770 260.768 330.652 290.807 410.451 360.747 370.659 120.545 360.924 720.473 330.149 780.571 430.811 280.635 500.746 360.623 230.892 310.794 460.570 56
PointConvpermissive0.666 350.781 290.759 300.699 490.644 330.822 260.475 250.779 280.564 440.504 530.953 190.428 540.203 570.586 380.754 350.661 380.753 330.588 410.902 220.813 350.642 30
Wenxuan Wu, Zhongang Qi, Li Fuxin: PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019
PointASNLpermissive0.666 350.703 590.781 220.751 400.655 270.830 190.471 270.769 310.474 680.537 390.951 230.475 320.279 270.635 250.698 480.675 320.751 340.553 540.816 660.806 370.703 15
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, Shuguang Cui: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. CVPR 2020
PPCNN++permissive0.663 370.746 440.708 510.722 420.638 350.820 290.451 360.566 730.599 300.541 370.950 270.510 230.313 140.648 220.819 260.616 560.682 600.590 390.869 510.810 360.656 25
Pyunghwan Ahn, Juyoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim: Projection-based Point Convolution for Efficient Point Cloud Segmentation. IEEE Access
DCM-Net0.658 380.778 300.702 540.806 210.619 390.813 390.468 290.693 530.494 610.524 450.941 530.449 460.298 200.510 600.821 250.675 320.727 430.568 470.826 630.803 390.637 32
Jonas Schult*, Francis Engelmann*, Theodora Kontogianni, Bastian Leibe: DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes. CVPR 2020 [Oral]
HPGCNN0.656 390.698 600.743 410.650 660.564 570.820 290.505 150.758 330.631 170.479 580.945 420.480 300.226 460.572 420.774 330.690 280.735 390.614 260.853 580.776 600.597 49
Jisheng Dang, Qingyong Hu, Yulan Guo, Jun Yang: HPGCNN.
SAFNet-segpermissive0.654 400.752 420.734 450.664 620.583 510.815 350.399 610.754 350.639 140.535 410.942 510.470 340.309 160.665 180.539 620.650 410.708 500.635 190.857 570.793 480.642 30
Linqing Zhao, Jiwen Lu, Jie Zhou: Similarity-Aware Fusion Network for 3D Semantic Segmentation. IROS 2021
RandLA-Netpermissive0.645 410.778 300.731 460.699 490.577 520.829 200.446 410.736 410.477 670.523 470.945 420.454 420.269 330.484 670.749 380.618 540.738 370.599 330.827 620.792 510.621 37
MVPNetpermissive0.641 420.831 170.715 490.671 590.590 470.781 570.394 630.679 560.642 130.553 340.937 590.462 380.256 380.649 210.406 760.626 510.691 570.666 120.877 410.792 510.608 42
Maximilian Jaritz, Jiayuan Gu, Hao Su: Multi-view PointNet for 3D Scene Understanding. GMDL Workshop, ICCV 2019
PointConv-SFPN0.641 420.776 320.703 530.721 430.557 600.826 220.451 360.672 580.563 450.483 570.943 500.425 570.162 730.644 230.726 400.659 390.709 490.572 440.875 430.786 550.559 61
PointMRNet0.640 440.717 560.701 550.692 520.576 530.801 450.467 310.716 460.563 450.459 630.953 190.429 530.169 700.581 390.854 150.605 570.710 470.550 550.894 290.793 480.575 54
FPConvpermissive0.639 450.785 280.760 290.713 470.603 420.798 490.392 640.534 780.603 280.524 450.948 330.457 400.250 400.538 540.723 420.598 610.696 550.614 260.872 480.799 400.567 58
Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han: FPConv: Learning Local Flattening for Point Convolution. CVPR 2020
PD-Net0.638 460.797 250.769 270.641 710.590 470.820 290.461 330.537 770.637 150.536 400.947 360.388 670.206 540.656 190.668 520.647 450.732 410.585 420.868 520.793 480.473 79
PointSPNet0.637 470.734 500.692 620.714 460.576 530.797 500.446 410.743 390.598 310.437 680.942 510.403 630.150 770.626 290.800 300.649 420.697 540.557 520.846 590.777 590.563 59
SConv0.636 480.830 180.697 580.752 390.572 560.780 590.445 430.716 460.529 520.530 420.951 230.446 480.170 690.507 620.666 530.636 490.682 600.541 610.886 350.799 400.594 50
Supervoxel-CNN0.635 490.656 650.711 500.719 440.613 400.757 680.444 460.765 320.534 510.566 310.928 700.478 310.272 300.636 240.531 640.664 360.645 710.508 680.864 540.792 510.611 39
joint point-basedpermissive0.634 500.614 730.778 230.667 610.633 370.825 230.420 540.804 230.467 700.561 320.951 230.494 260.291 220.566 440.458 700.579 670.764 290.559 510.838 600.814 330.598 48
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu, Winston H. Hsu: A Unified Point-Based Framework for 3D Segmentation. 3DV 2019
MCCNNpermissive0.633 510.866 100.731 460.771 310.576 530.809 400.410 560.684 540.497 600.491 550.949 300.466 360.105 830.581 390.646 560.620 520.680 620.542 600.817 650.795 440.618 38
P. Hermosilla, T. Ritschel, P.P. Vazquez, A. Vinacua, T. Ropinski: Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds. SIGGRAPH Asia 2018
PointMTL0.632 520.731 510.688 650.675 560.591 460.784 560.444 460.565 740.610 220.492 540.949 300.456 410.254 390.587 360.706 450.599 600.665 670.612 290.868 520.791 540.579 53
3DSM_DMMF0.631 530.626 700.745 390.801 230.607 410.751 690.506 140.729 440.565 430.491 550.866 860.434 490.197 610.595 340.630 570.709 220.705 520.560 490.875 430.740 700.491 74
APCF-Net0.631 530.742 470.687 670.672 570.557 600.792 540.408 570.665 590.545 480.508 500.952 220.428 540.186 640.634 260.702 460.620 520.706 510.555 530.873 460.798 420.581 52
Haojia, Lin: Adaptive Pyramid Context Fusion for Point Cloud Perception. GRSL
PointNet2-SFPN0.631 530.771 340.692 620.672 570.524 640.837 140.440 480.706 510.538 500.446 650.944 480.421 590.219 490.552 500.751 370.591 630.737 380.543 590.901 240.768 620.557 62
FusionAwareConv0.630 560.604 750.741 430.766 350.590 470.747 700.501 160.734 420.503 590.527 430.919 760.454 420.323 110.550 530.420 750.678 310.688 580.544 570.896 270.795 440.627 36
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng, Kai Xu: Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation. CVPR 2020
DenSeR0.628 570.800 240.625 780.719 440.545 620.806 420.445 430.597 670.448 750.519 480.938 580.481 290.328 90.489 660.499 690.657 400.759 310.592 380.881 390.797 430.634 33
SegGroup_sempermissive0.627 580.818 210.747 380.701 480.602 430.764 650.385 690.629 640.490 630.508 500.931 690.409 620.201 580.564 450.725 410.618 540.692 560.539 620.873 460.794 460.548 65
An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, Jie Zhou: SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation. TIP 2022
SIConv0.625 590.830 180.694 600.757 370.563 580.772 630.448 400.647 620.520 540.509 490.949 300.431 520.191 620.496 640.614 580.647 450.672 650.535 640.876 420.783 560.571 55
HPEIN0.618 600.729 520.668 680.647 680.597 450.766 640.414 550.680 550.520 540.525 440.946 390.432 500.215 510.493 650.599 590.638 480.617 760.570 450.897 260.806 370.605 45
Li Jiang, Hengshuang Zhao, Shu Liu, Xiaoyong Shen, Chi-Wing Fu, Jiaya Jia: Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation. ICCV 2019
SPH3D-GCNpermissive0.610 610.858 130.772 250.489 830.532 630.792 540.404 600.643 630.570 420.507 520.935 620.414 610.046 890.510 600.702 460.602 590.705 520.549 560.859 560.773 610.534 68
Huan Lei, Naveed Akhtar, and Ajmal Mian: Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds. TPAMI 2020
AttAN0.609 620.760 390.667 690.649 670.521 650.793 520.457 350.648 610.528 530.434 700.947 360.401 640.153 760.454 690.721 430.648 440.717 450.536 630.904 200.765 630.485 75
Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu and Qigong Sun: AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation. IJCAI2020
wsss-transformer0.600 630.634 680.743 410.697 510.601 440.781 570.437 500.585 710.493 620.446 650.933 670.394 650.011 910.654 200.661 550.603 580.733 400.526 650.832 610.761 650.480 76
LAP-D0.594 640.720 540.692 620.637 720.456 740.773 620.391 660.730 430.587 330.445 670.940 550.381 680.288 230.434 720.453 720.591 630.649 690.581 430.777 700.749 690.610 41
DPC0.592 650.720 540.700 560.602 760.480 700.762 670.380 700.713 490.585 360.437 680.940 550.369 700.288 230.434 720.509 680.590 650.639 740.567 480.772 710.755 670.592 51
Francis Engelmann, Theodora Kontogianni, Bastian Leibe: Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds. ICRA 2020
CCRFNet0.589 660.766 380.659 720.683 540.470 730.740 720.387 680.620 660.490 630.476 590.922 740.355 740.245 430.511 590.511 670.571 680.643 720.493 720.872 480.762 640.600 47
ROSMRF0.580 670.772 330.707 520.681 550.563 580.764 650.362 720.515 790.465 710.465 620.936 610.427 560.207 530.438 700.577 600.536 720.675 640.486 730.723 770.779 570.524 70
SD-DETR0.576 680.746 440.609 820.445 870.517 660.643 830.366 710.714 480.456 720.468 610.870 850.432 500.264 360.558 480.674 500.586 660.688 580.482 750.739 750.733 720.537 67
SQN_0.1%0.569 690.676 620.696 590.657 640.497 670.779 600.424 520.548 750.515 560.376 750.902 830.422 580.357 30.379 760.456 710.596 620.659 680.544 570.685 800.665 840.556 63
TextureNetpermissive0.566 700.672 640.664 700.671 590.494 680.719 730.445 430.678 570.411 810.396 730.935 620.356 730.225 470.412 740.535 630.565 690.636 750.464 770.794 690.680 810.568 57
Jingwei Huang, Haotian Zhang, Li Yi, Thomas Funkerhouser, Matthias Niessner, Leonidas Guibas: TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes. CVPR
DVVNet0.562 710.648 660.700 560.770 320.586 500.687 770.333 760.650 600.514 570.475 600.906 800.359 720.223 480.340 790.442 740.422 830.668 660.501 690.708 780.779 570.534 68
Pointnet++ & Featurepermissive0.557 720.735 490.661 710.686 530.491 690.744 710.392 640.539 760.451 740.375 760.946 390.376 690.205 550.403 750.356 790.553 710.643 720.497 700.824 640.756 660.515 71
PointMRNet-lite0.553 730.633 690.648 730.659 630.430 770.800 460.390 670.592 690.454 730.371 770.939 570.368 710.136 800.368 770.448 730.560 700.715 460.486 730.882 370.720 760.462 80
GMLPs0.538 740.495 840.693 610.647 680.471 720.793 520.300 790.477 800.505 580.358 780.903 820.327 770.081 860.472 680.529 650.448 810.710 470.509 660.746 730.737 710.554 64
PanopticFusion-label0.529 750.491 850.688 650.604 750.386 800.632 840.225 890.705 520.434 780.293 840.815 870.348 750.241 440.499 630.669 510.507 740.649 690.442 830.796 680.602 870.561 60
Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji: PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things. IROS 2019 (to appear)
subcloud_weak0.516 760.676 620.591 850.609 730.442 750.774 610.335 750.597 670.422 800.357 790.932 680.341 760.094 850.298 810.528 660.473 790.676 630.495 710.602 860.721 750.349 87
Online SegFusion0.515 770.607 740.644 760.579 780.434 760.630 850.353 730.628 650.440 760.410 710.762 900.307 790.167 710.520 570.403 770.516 730.565 790.447 810.678 810.701 780.514 72
Davide Menini, Suryansh Kumar, Martin R. Oswald, Erik Sandstroem, Cristian Sminchisescu, Luc van Gool: A Real-Time Learning Framework for Joint 3D Reconstruction and Semantic Segmentation. Robotics and Automation Letters Submission
3DMV, FTSDF0.501 780.558 800.608 830.424 890.478 710.690 760.246 850.586 700.468 690.450 640.911 780.394 650.160 740.438 700.212 860.432 820.541 840.475 760.742 740.727 730.477 77
PCNN0.498 790.559 790.644 760.560 800.420 790.711 750.229 870.414 810.436 770.352 800.941 530.324 780.155 750.238 860.387 780.493 750.529 850.509 660.813 670.751 680.504 73
3DMV0.484 800.484 860.538 870.643 700.424 780.606 880.310 770.574 720.433 790.378 740.796 880.301 800.214 520.537 550.208 870.472 800.507 880.413 860.693 790.602 870.539 66
Angela Dai, Matthias Niessner: 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation. ECCV'18
PointCNN with RGBpermissive0.458 810.577 780.611 810.356 910.321 880.715 740.299 810.376 850.328 880.319 820.944 480.285 820.164 720.216 890.229 840.484 770.545 830.456 790.755 720.709 770.475 78
Yangyan Li, Rui Bu, Mingchao Sun, Baoquan Chen: PointCNN. NeurIPS 2018
FCPNpermissive0.447 820.679 610.604 840.578 790.380 810.682 780.291 820.106 910.483 660.258 890.920 750.258 860.025 900.231 880.325 800.480 780.560 810.463 780.725 760.666 830.231 91
Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari: Fully-Convolutional Point Networks for Large-Scale Point Clouds. ECCV 2018
DGCNN_reproducecopyleft0.446 830.474 870.623 790.463 850.366 830.651 810.310 770.389 840.349 860.330 810.937 590.271 840.126 810.285 820.224 850.350 880.577 780.445 820.625 840.723 740.394 83
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon: Dynamic Graph CNN for Learning on Point Clouds. TOG 2019
PNET20.442 840.548 810.548 860.597 770.363 840.628 860.300 790.292 860.374 830.307 830.881 840.268 850.186 640.238 860.204 880.407 840.506 890.449 800.667 820.620 860.462 80
SurfaceConvPF0.442 840.505 830.622 800.380 900.342 860.654 800.227 880.397 830.367 840.276 860.924 720.240 870.198 600.359 780.262 820.366 850.581 770.435 840.640 830.668 820.398 82
Hao Pan, Shilin Liu, Yang Liu, Xin Tong: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames.
Tangent Convolutionspermissive0.438 860.437 890.646 750.474 840.369 820.645 820.353 730.258 880.282 900.279 850.918 770.298 810.147 790.283 830.294 810.487 760.562 800.427 850.619 850.633 850.352 86
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou: Tangent convolutions for dense prediction in 3d. CVPR 2018
3DWSSS0.425 870.525 820.647 740.522 810.324 870.488 910.077 920.712 500.353 850.401 720.636 920.281 830.176 670.340 790.565 610.175 920.551 820.398 870.370 920.602 870.361 85
SPLAT Netcopyleft0.393 880.472 880.511 880.606 740.311 890.656 790.245 860.405 820.328 880.197 900.927 710.227 890.000 930.001 930.249 830.271 910.510 860.383 890.593 870.699 790.267 89
Hang Su, Varun Jampani, Deqing Sun, Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan Yang, Jan Kautz: SPLATNet: Sparse Lattice Networks for Point Cloud Processing. CVPR 2018
ScanNet+FTSDF0.383 890.297 910.491 890.432 880.358 850.612 870.274 830.116 900.411 810.265 870.904 810.229 880.079 870.250 840.185 890.320 890.510 860.385 880.548 880.597 900.394 83
PointNet++permissive0.339 900.584 770.478 900.458 860.256 910.360 920.250 840.247 890.278 910.261 880.677 910.183 900.117 820.212 900.145 910.364 860.346 920.232 920.548 880.523 910.252 90
Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas: pointnet++: deep hierarchical feature learning on point sets in a metric space.
SSC-UNetpermissive0.308 910.353 900.290 920.278 920.166 920.553 890.169 910.286 870.147 920.148 920.908 790.182 910.064 880.023 920.018 930.354 870.363 900.345 900.546 900.685 800.278 88
ScanNetpermissive0.306 920.203 920.366 910.501 820.311 890.524 900.211 900.002 930.342 870.189 910.786 890.145 920.102 840.245 850.152 900.318 900.348 910.300 910.460 910.437 920.182 92
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner: ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR'17
ERROR0.054 930.000 930.041 930.172 930.030 930.062 930.001 930.035 920.004 930.051 930.143 930.019 930.003 920.041 910.050 920.003 930.054 930.018 930.005 930.264 930.082 93